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Record W3134200745 · doi:10.3168/jds.2020-19390

Controlled trial of the effect of negative dietary cation-anion difference prepartum diets on milk production, reproductive performance, and culling of dairy cows

2021· article· en· W3134200745 on OpenAlexafffundabout
Rita Couto Serrenho, T.C. Bruinjé, E.I. Morrison, T.J. DeVries, T.F. Duffield, S.J. LeBlanc

Bibliographic record

VenueJournal of Dairy Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsCullingAnimal scienceDry matterIce calvingLactationDairy cattleInseminationLogistic regressionPregnancyMilk productionBiologyMedicineHerdInternal medicine

Abstract

fetched live from OpenAlex

Our objective was to assess the effects of feeding negative dietary cation-anion difference (DCAD) prepartum diets on milk production, reproductive performance, and culling. Cows from 4 commercial farms in Ontario, Canada were enrolled in a pen-level controlled trial from November 2017 to April 2019. Close-up pens (1 per farm) with cows 3 wk before calving were randomly assigned to a negative DCAD (TRT; -108 mEq/kg of dry matter; target urine pH 6.0-6.5) or a control diet (CON; +105 mEq/kg of dry matter with a placebo supplement). Each pen was fed TRT or CON for 3 mo (1 period), and then switched to the other treatment for the next period (4 periods per farm). Data from 15 experimental units (8 pen treatments in TRT and 7 in CON), with a total of 1,086 observational units (cows), were included. The effect of treatment on milk yield at the first 3 milk recording tests of lactation was assessed with linear regression models accounting for repeated measures. The risk of pregnancy at first artificial insemination and culling by 30, 60, and 305 d in milk (DIM) were analyzed with logistic regression models, and effects on time to first AI, pregnancy, and culling were assessed with Cox proportional hazards models. All models included treatment, parity, and their interactions, accounting for pen-level randomization and clustering of animals within farm with random effects, giving 10 degrees of freedom for treatment effects. Multiparous cows fed TRT produced more milk at the first (42.0 vs. 38.8 ± 1.2 kg/d) and second (44.2 vs. 41.7 ± 1.3 kg/d) milk tests. However, multiparous cows fed TRT tended to have 0.2 percentage units less milk fat content at these tests. Although multiparous cows fed TRT tended to have greater energy-corrected milk at the first test (least squares means ± standard error: TRT = 46.1 ± 0.9 vs. CON = 43.8 ± 1 kg/d), there were no differences observed in energy-corrected milk at the second or third tests. In primiparous cows, there was no effect of treatment on milk production. Multiparous cows fed TRT had greater pregnancy to first insemination (TRT = 42 ± 3 vs. CON = 32 ± 4%) and tended to have shorter time to pregnancy [hazard ratio (HR) = 1.20; 95% CI: 0.96-1.49]. In primiparous cows fed TRT, time to pregnancy was increased (HR = 0.76; 95% CI: 0.59-0.99). Culling by 30 DIM tended to be less in TRT (3.3 ± 1.1%) than CON (5.5 ± 1.8%). No effect of treatment on culling by 305 DIM was detected in primiparous cows, but in multiparous cows, the TRT diets decreased the odds of culling (21.3 ± 1.9 vs. 31.7 ± 2.8%) and daily risk of culling to 305 DIM (HR = 0.64; 95% CI: 0.46 to 0.89). Under commercial herd conditions, prepartum negative DCAD diets improved milk production and reproductive performance, and reduced culling risk in multiparous cows. In primiparous cows, TRT diets had no effect on milk yield or culling, but increased the time to pregnancy. Our results suggest that negative DCAD diets should be targeted to multiparous cows.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.255
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2021
Admission routes3
Has abstractyes

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